Cohere releases Embed 4, a multimodal model for enterprise document search

Cohere has introduced Embed 4, a multimodal language model designed for semantic search across complex enterprise documents. The model can process a wide range of content types—including text, images, tables, charts, code, and handwritten scans—commonly found in financial reports, medical records, and industrial documentation. Embed 4 supports files up to 128,000 tokens, or approximately 200 pages, and is compatible with over 100 languages, including Arabic, French, and Japanese. According to Cohere, the model is intended for organizations building language model-powered assistants that require access to internal knowledge. The model can be deployed either on-premises or in a private cloud environment, a configuration aimed at sectors with strict data sensitivity requirements, such as healthcare and manufacturing. Cohere says Embed 4 is now available through its own platform, as well as via Microsoft Azure AI Foundry and Amazon SageMaker.